This report is generated from several Rmarkdown documents where each chapter is a separate markdown report. The folowing are the Rmd files used in analyzing the data.

  1. check_seData/SE_Data_2015.Rmd
  2. trnCompare/trnCompare.Rmd
  3. extract_SERPM_SEData/check_se_data_trend.Rmd
  4. highway_stats/highway_stats.Rmd

Chapter-1: Review of Socio-Demographic Input Data

1. Introduction

Transit ridership and highway volumes for the Corridor studies are obtained by running various alternative scenarios in STOPS and SERPM models. Both of these models are developed differently and uses different input data sets. However, some of the data between these models come from MPO and transit agencies. The transit data between STOPS and SERPM is in two different formats. STOPS transit inputs are in GTFS format whereas SERPM inputs are in CUBE PT format. Although these two are not comparable formats, they both should represent transit networks. The other input file is socio-demographic or landuse file. The landuse file exists in both model but again in different format and at different geographic levels. Since the transit ridership estimates for the Corridor studies are expected to reply upon STOPS model, transit networks between STOPS and SERPM are not compared here. However, the landuse data is studied in great detail.

The 2015 socio-economic data is developed through linear interpolation. Currently, the 2015 SE data exists in two different models:

  1. SEFL STOPS model (MPO0000TAZPopEmp.dbf)
  2. SERPM MAZ data (maz_data.csv)

The STOPS SE data is at TAZ level where as SERPM model data is at MAZ level. In order to perform a comparative analysis between these two input data, SERPM MAZ level data is aggregated to TAZ. This document shows all findings at TAZ level. Technically SE data in the two models should be same and should originate from the same source. Since both models (STOPS and SERPM) were constantly being updated the model data for 2040 could be different. This document summarizes those differences.

The 2040 SE data exists in the following model locations:

  1. SERPM v7.602 from FDOT website
  2. Corradino delivered model
  3. SEFL STOPS
#path <- getwd()
path <-"/Volumes/C/projects/SERPM_Compare/check_seData"

# data directories
dir <- "Corradino_SEData"
fdot.dir <- "FDOT_June_30_2016"
stops.dir <- "STOPS_SEData"
  
# file names 
# TODO (ans): Replace maz_data.csv files with model_data.csv (which is more comprehensive data)
maz.data.files <- c("2010_maz_data.csv", "2015_maz_data.csv", "2040_maz_data.csv")
fdot.maz.files <- c("maz_data_IN_2040R.csv", "maz_data_IN_2040T.csv")
stops_mpo_shapeFile <- "simplified_MPOTAZPopEmp.shp"
taz_county_file <- "taz_county.csv"

# list of TAZs to check
check_taz <- c(76, 387, 979, 1596, 1598, 2253)

# Save R Objects for later use
save.RData.outputs <- TRUE

2. SERPM Landuse Data

The SERPM maz data files are developed by FDOT with feedback from various agencies, including the three MPOs in the region. The future year maz data file is constantly updated to reflect revised population and employment projections. Due to this continuous update there are several versions of 2040 data with significant difference across population, households and employment variables. As a part of Corridor studies effort, it is required to document the source of model data being used as well as validate data.

The model data files delivered by Corradino were reviewed for data consistency across the three horizon years: 2010, 2015 and 2040. Some of the data fields are not consistent across all years. Two fields geoSRate and geoSRateNm exist in some maz_data.csv files but not in all.

2.1 Check 2015 Trend

The 2015 SE data is developed through linear interpolation and thus the growth rate should always be linear and between 2010 and 2040. This section of the code checks if there are any households in 2015 that drop from 2010 but gain back in 2040 (checks growth rate for linearity). The following table shows households across 2010, 2015 and 2040 (data with 5 hhs difference is ignored).

** The household variable in this maz_data.csv file computed by aggregating PopSyn-3 outputs and thus there is some degree of over/under estimation of households at MAZ level when compared to PopSyn-3 inputs ** The differences shown in the data below are within reasonable range.

check_hh <- data_all_years %>%
   mutate_each(funs(replace(.,is.na(.),0))) %>%
   mutate(diff_1015 = hh_2015 - hh_2010,
          diff_1540 = hh_2040 - hh_2015,
          check = ifelse((diff_1015 > 0 && diff_1540 < 0) || (diff_1015 < 0 && diff_1540 > 0), 1, 0)) %>%
   filter(check == 1 , abs(diff_1540) > 5, abs(diff_1015) > 5)

check_hh <- check_hh %>%
    select(TAZ, hh_2010,    hh_2015,    hh_2040, diff_1015, diff_1540)

# kable(check_hh, caption = "Zones with Inconsistent Households Trends", digits = 0, format.args = list(big.mark = ","))

datatable(check_hh, caption = "Zones with Inconsistent Households Trends")
# Save R Object file
 if (save.RData.outputs) {
   save(check_hh, file = "table_check_hh.RData")
 }

2.2 Selected TAZs

The following table shows data for selected TAZ: 76, 387, 979, 1596, 1598, 2253. These zones were selected based on the past review of 2015 zonal data. The current model data shows consistent growth rate across household, population and employment variables between 2010, 2015 and 2040 years.

sel_data <- data_all_years %>% 
   filter(TAZ %in% check_taz) %>%
   select(TAZ, pop_2010, pop_2015, pop_2040, 
               emp_total_2010, emp_total_2015, emp_total_2040, 
               hh_2010, hh_2015,    hh_2040)

kable(sel_data, caption = "Selected TAZ from SERPM Data", digits = 0, format.args = list(big.mark = ","))
Selected TAZ from SERPM Data
TAZ pop_2010 pop_2015 pop_2040 emp_total_2010 emp_total_2015 emp_total_2040 hh_2010 hh_2015 hh_2040
76 953 994 1,207 119 123 142 569 597 745
387 214 217 217 745 730 655 98 98 99
979 1,689 1,684 1,690 243 244 251 788 787 780
1,596 499 505 499 402 417 493 241 241 244
1,598 307 382 656 27 28 31 165 173 241
2,253 34 80 277 265 267 275 15 33 122

2.3 Growth Rate Analysis

The figures below show growth rates by county for selected input variables: households (hh), population (pop), total employment (emp_total), college enrollement (college), school enrollement (school).

  1. Miami-Dade County: The growth rate looks ok here. The hh, pop and emp growth rates are at 25 percent, college enrollment grwoth is at 20 percent and school is at 5 percent.

  2. Broward County: The hh growth rate looks ok, pop seems a bit low at 12 percent but employment is projected to grow by 5 percent? Need to double check with FDOT. Same issue with College and School too.

  3. Palm-Beach County: The growth rate looks ok here. The hh, pop and emp growth rates are at 30 percent, college and school at 30 and 15 percent respectively.

3. FDOT Official 2040 Data

The two official 2040 maz_data.csv files are downloaded from FDOT website June 2016 :

  1. IN-2040T_data.csv : 2040 LRTP scenario data
  2. IN-2040R_data.csv : 2040 Cost Feasible scenario data

These two data sets are compared with the Corradino delivered 2040 data to make sure the model is using the official version and to document the source of model data being used for the corridor studies.

3.1 Compare 2040 CF with Corradino data

Several tabulations were computed to ensure that the model data being used for the corridor studies is from the official FDOT Cost Feasible scenario. Table below shows a comparison between the 2040 Cost Feasible and Corradino 2040 model data, where there are zero differences. Chart below shows a scatter plot of population variable from the two data sets where there it clearly depicts both data sets being same.

mgra TAZ.Corradino hh.Corradino pop.Corradino emp_total.Corradino TAZ.FDOT hh.FDOT pop.FDOT emp_total.FDOT diff.hh diff.pop diff.emp pop.bin —– ————– ————- ————– ——————– ——— ——– ——— ————— ——– ——— ——— ——–

3.2 2040 LRTP vs 2040 Cost Feasible

Well, the 2040 LRTP data is significantly different from 2040 Cost Feasible and thus the Corradino data also differed. There are too many zones to display the difference in tabular form. The plot below shows a scatter plot of population data from the two data sets.

mgra TAZ.Corradino hh.Corradino pop.Corradino emp_total.Corradino TAZ.FDOT hh.FDOT pop.FDOT emp_total.FDOT diff.hh diff.pop diff.emp pop.bin
1 2901 43 169 0 2901 43 172 0 0 -3 0 -1000 to -1
2 2902 9 23 1337 2902 9 23 1337 0 0 0 0
3 2903 497 1694 379 2903 497 1685 379 0 9 0 1 to 1000
4 2903 273 984 21 2903 273 1001 21 0 -17 0 -1000 to -1
5 2903 383 1306 86 2903 383 1308 86 0 -2 0 -1000 to -1
6 2903 212 861 14 2903 212 841 14 0 20 0 1 to 1000

4. STOPS MPO Data

The latest South East Florida Regional STOPS model is downloaded from FDOT page and was reviewed. As a part of the review, model landuse data and observed APC counts were checked. Since the Corridor studies use both SERPM and STOPS models, it is important to check and ensure the input data is consistent between the models. The downloaded SEFL STOPS model consists of 2010, 2015, and 2040 population and employment data at TAZ level. As per SEFL STOPS model documentation, the 2014 data computed by interpolating between 2010 and 2040. STOPS model utilizes only population and employment variables and household variable is not used and thus not provided in the data set.

This data is clearly different from SERPM 2015 MAZ data.

# Read data from stops input
shape <- readOGR(paste0(path,"/",stops.dir,"/",stops_mpo_shapeFile), layer = "simplified_MPOTAZPopEmp", verbose = FALSE)
stops_se <- shape@data

stops_sel_data <- stops_se %>%
  filter(TAZ_REG %in% check_taz) %>%
  select(TAZ_REG, POP_10,   POP_15, POP_40, 
                  TOTE_10, TOTE_15, TOTE_40)

kable(stops_sel_data, caption = "Selected TAZ from STOPS Data", digits = 0, format.args = list(big.mark = ","))
Selected TAZ from STOPS Data
TAZ_REG POP_10 POP_15 POP_40 TOTE_10 TOTE_15 TOTE_40
76 953 994 1,202 119 123 142
387 214 214 216 745 730 655
979 1,689 1,689 1,691 243 244 251
1,596 499 498 495 402 417 493
1,598 307 364 649 27 28 31
2,253 34 75 279 265 267 275

4.1 2015 population and employment difference

Table below shows the number of TAZ by range of pop and emp difference. If there is no difference then it is not tabulated here.

bin diff.pop_15 diff_emp_15
-100 to -50 14 NA
-20 to 0 1445 177
-50 to -20 50 NA
-500 to -100 21 NA
-5000 to -500 5 NA
0 to 20 1496 885
100 to 500 27 2
20 to 50 88 1
50 to 100 44 NA
500 to 5000 2 1

The plot below shows population difference between the two data sets.

The plot below shows employment difference between the two data sets.

4.2 Population Difference

The map below shows population difference for 2010, 2015 and 2040 by TAZ between the two data sets. The population for 2010 between the two models (SERPM and STOPS) is the same data where as the 2040 data is different across most of the TAZs. I guess the 2040 difference trickled down into 2015 when interpolated.

4.3 Employment Difference

The employment data for 2010 between the two models (SERPM and STOPS) is the same data where as the 2015 data is different across most of the TAZs. About 5 TAZs show varying employment data for year 2040. Table below shows those 5 TAZs.

 tabulate_emp_diff_2040 <- df %>% 
      filter(diff_emp_40 != 0) %>%
      select(TAZ, emp_total_2040, TOTE_40, diff_emp_40)
 
  kable(tabulate_emp_diff_2040)
TAZ emp_total_2040 TOTE_40 diff_emp_40
825 8160 250 7910
854 89 0 89
864 3070 386 2684
1058 3000 0 3000
2409 1831 1678 153

The map below shows population difference for 2010, 2015 and 2040 by TAZ between the two data sets.

Chapter-2: SERPM Transit Routes

1. Transit Network

In the current SERPM version there are 4 transit networks:

  • Two for transit scenarios (2040B is for corridors and 2040R represents cost feasible) for 2040,
  • One 2015 and
  • One 2010

Note: Cube catalog shows one another scenario: 2040 LRTP (2040T), for which no input files are provided.

2. Data Processing

CUBE Transit Network are stored in LIN format and contains both route attributes and stop attributes in the same file. In order to use better use this information for comparative analysis as well as display subjective routes, the LIN files need to be converted to spatial data formats such as shape files. Converting this is a four step process:

  1. Open CUBE and create an empty MDB file (ex: test.MDB)
  2. Open alternative transit network along with corresponding highway network in CUBE.
  3. Open exported wizard from File > Export > GIS Database.
  • First browse the highway network and export it to test.MDB file
  • Next export transit network to same test.MDB but with above hwy network as base network
  1. Open test.MDB in CUBE, select transit node and line layers and export to shape file.

There are a total of 624 routes across these four networks.

2. Route Comparisons

This document compares all transit networks and summarizes the differences. The following are the different tabulations:

  1. Routes present in all scenarios
  2. Routes in 2010 and 2040CF but removed from 2015 and 2040TC (for transit corridors)
  3. Routes not in 2010 or 2040CF but added to 2015 and 2040TC
  4. Routes in 2015 but not in 2040TC (and not in 2010 or 2040CF)

3. Routes present in all scenarios

There are about 473 routes that exists in all scenarios. Although these routes have common name, some of the route attributes vary in Mode and Headways. Some of the routes also show minor alignment changes and those routes are compared to better understand the alignment changes. Route alignments over 1 mile (100 cube spatial units) are plotted.

Mode Comparison

Table List of routes by mode shows the number of routes by mode across all alternatives. The common routes between 2010 and 2015 didn’t show any differences in the attributes. Although these routes are listed as common routes, there is a mode code difference for about 12 routes between 2010/2015 and 2040CF/2040TC alternatives. All of the differences are in BRT, Express Bus and Local Bus reports.

List of common routes with mode change between 2010/2015 and 2040CF/2040TC

Map of common routes with mode change between 2010/2015 and 2040CF/2040TC

Spatial Alignment Comparison

List of common routes with ALIGNMENT CHANGE change between 2010/2015 and 2040CF/2040TC

Map of common routes with ALIGNMENT CHANGE over 1 mile are plotted below. Four comparison sets are shown below:

  1. 2010 vs 2015: where 2010 (2010 vs 2015) shows 2010 routes and 2015 (2010 vs 2015) shows 2015 routes.
  2. 2010 vs 2040CF: where 2010 (2010 vs 2040CF) shows 2010 routes and 2040CF (2010 vs 2040CF) shows 2040CF routes.
  3. 2015 vs 2040TC: where 2015 (2015 vs 2040TC) shows 2015 routes and 2040TC (2015 vs 2040TC) shows 2040TC routes.
  4. 2040CF vs 2040TC: where 2040CF (2040CF vs 2040TC) shows 2040CF routes and 2040TC (2040CF vs 2040TC) shows 2040TC routes.

Headway Comparison

Headway_1

List of common routes with HEADWAY_1 differences

Although these routes exists in all scenarios, the differences between any two scenario networks is very vast. There are about 106 common routes with different headways between 2010 and 2015. The plot below shows these routes along with the headways (click on the link to popup the attributes). The headway differences are printed to csv files for future reference.

Similar to 2010 vs 2015, other common routes from other scenarios are also compared. Check / uncheck the map keys to reveal this information.

Headway_2

List of common routes with HEADWAY_2 differences Although these routes exists in all scenarios, the differences between any two scenario networks is very vast. There are about 86 common routes with difference in headways (HEADWAY_2) between 2010 and 2015. The plot below shows these routes along with the headways (click on the link to popup the attributes). The headway differences are printed to csv files for future reference.

Similar to 2010 vs 2015, other common routes from other scenarios are also compared. Check / uncheck the map keys to reveal this information.

Headway_3

List of common routes with HEADWAY_3 differences Although these routes exists in all scenarios, the differences between any two scenario networks is very vast. There are about 127 common routes with difference in headways (HEADWAY_3) between 2010 and 2015. The plot below shows these routes along with the headways (click on the link to popup the attributes). The headway differences are printed to csv files for future reference.

Similar to 2010 vs 2015, other common routes from other scenarios are also compared. Check / uncheck the map keys to reveal this information.

4. 2010 / 2040 CF Exclusive: Routes not in 2015 / 2040 TC

There are about six routes that exist in 2010 and 2040CF that are not in 2015 and 2040TC. The table below shows the list of these routes along with 2010 and 2040 headway and mode. These routes contain identical attributes between 2010 and 2040.

5. 2015 / 2040 TC Exclusive: Routes not in 2010 / 2040 CF

There are about 22 routes that are present in 2015 and 2040 TC but not in 2010 and 2040CF. These routes are added to support the Flagler Corridor. The routes are located in the City of Doral and Downtown area, which are western and eastern ends of the Corridor. The mode and headway between 2015 and 2040TC for these routes are identical.

6. 2015 Exclusive: Routes not in 2010 / 2040 CF / 2040 TC

There are no routes in 2015 that doesn’t exist in any of the scenarios.

Chapter-4: ODME Validation

1. Data

The following are the list of data files used for the analysis in this document.

DAILY_TEM4_300Iter.dbf : is the final ODME subarea loaded network. The initial settings of 12 loops showed the ODME process did not converge, where the %RMSE between the Count and Volume was checked. The convergence criteria was set at 15% for subarea wide RMSE. This condition was not met within default 12 iterations and thus additional iterations up to 300 feedback loops were run. The %RMSE at the end of 12 iteration was only 27% which clearly shows the ODME needs to be re-run. At the end of 300th iteration, the %RMSE is still at 17% and typically for a subarea wide it should be under 14%. Meaning more iterations can be run, however, before making that call, we need to understand the ODME results and why it didn’t converge.

before_ODME.dbf : is the processed subarea network links with volumes prior to ODME. The networks are time period specific and carry regional nodes numbering. The subarea network file with old and new nodes is used to convert the regional networks to one single dbf file with just volume and counts by time periods.

regional_before_ODME.dbf : is the final loaded regional assignment network. The links and nodes are subset to represent subarea region. Technically before_ODME.dbf and regional_before_ODME.dbf should be identical, however, they are not although very close. Most of the tables show both before ODME volumes.

DAILY_TEM4_Link.shp : final ODME results, same as DAILY_TEM4_300Iter.dbf but a shape file.

PB_Flagler_Count_Location.csv : List of subarea links where PB collected traffic counts.

PB_Flagler_Counts.csv : PB counts

Flagler_Subnet_Links.csv : All Flagler Corridor links where there are counts (not PB collected counts).

2. Introduction

2.1 Origin Destination Matrix Estimation (ODME)

The latest delivered model from Feb 08, 2017 was run with 2010 seed skim with ODME turned on. The ODME procedure was applied by time period. For each of the time periods, root mean square error was generated by volume group, facility type and area type. However, the final validation statistics were computed on the daily results, which are not a true depiction of the ODME results. Thus additional statistics such as Root Mean Square Error (RMSE and %RMSE), Correlation Coefficient R2 were computed by time periods.

Additionally, stats from the before ODME procedure were looked to assess the ODME improvement. There are two way to get loaded network before ODME process.

Method 1: Regional Assignment

The before ODME volumes were extracted from the final loaded network of the regional highway assignment **{OUTDIR}_LOADED.net** . However, the counts in this network seemed not updated and are different from the subarea network counts. For this reason, the ODME before and after comparisons with this network may not give correct assessment of the ODME improvements.

Method 2: Extract Subarea OD Tables

Before initiating the ODME procedure, the subarea OD trip tables were generated for each time period by assigning the regional OD trip tables to the regional network with subarea network option turned on. This resulted in a subarea OD matrix as well as a loaded regional network for each time period. The following loaded regional networks were exported to dbf links and post processed to retain volumes.

  1. EA - {OUTDIR}1HWY00D.NET
  2. AM - {OUTDIR}1HWY00A.NET
  3. MD - {OUTDIR}1HWY00E.NET
  4. AM - {OUTDIR}1HWY00F.NET
  5. AM - {OUTDIR}1HWY00G.NET

In order to compare the link volume from the regional model (before ODME) to subarea model (after ODME), the links nodes should be mapped. This mapping is done by exporting subarea.net to both link.dbf and node.dbf files, where node.dbf contains node correspondence between regional and subarea networks, and link.dbf contains the links that can be be compared to subarea ODME results.

2.2 ODME in CUBE

ODME process in cube has many options to choose the targets from links counts, screen-lines, trip ends (zonal origins and destinations), paths, previously developed OD matrix and combination of these. The general method is the loaded network is converted into a specific format called ICM file, for efficient use where volumes, counts and other path choice settings are specified. Along with this file, screen-lines (link count locations) can be supplied and OD trip ends can be supplied. Each of these targets contains confidence thresholds that sets priority to targets. For this application the following targets and confidences were used:

  1. Link Counts (Screen-line) with confidence of 1000 to 5000 (highest)
  2. OD trip ends (subarea OD developed from regional model) with confidence of 100 (second level)
  3. OD trip interchanges (cells) with confidence of 1 (lowest priority)

High level overview of the process:

  1. Extract Subarea network (user defined boundary). Typically this process extracts OD table but here, it is used (or not done this way and Subarea OD is extracted separately)
  2. Add additional counts to the subarea network
  3. Extract Subarea OD tables via highway assignment. Inputs are regional networks, subarea network from step 2 and regional OD tables. The resulting outputs are Subarea OD tables and regional network with loaded volumes. The assignment rel-gap and max iterations are same as regional model.
  4. Convert loaded networks to ICM format and develop ODME targets: screen-line count locations, and subarea OD trip ends. This process is done through highway assignment. The assignment rel-gap and max iterations are slightly different, which should have been the same as above if this only for data conversion step.
  5. ODME process: Cube manual shows variety of optimization procedures and standard reporting outputs. The reports produced here shows ODME stats by RMSE, %RMSE and R2.
  6. Iterative: Feedback to step 4. This entire process is repeated till the RMSE from Step 4 is under 15. The RMSE is computed between Volumes and Counts, which is similar to RMSE from 5.

So, the only standard report from ODME process is the report produced in step 5 where the process lists ODME results for each ODME iteration. The convergence criteria used here are

3. AM Period

Some of the stats: observed counts, model volumes, RMSE, %RMSE, R-Squared and Scattered Plots were computed by:

  • By Volume Group
  • By Facility Type
  • By Area Type

for the following time periods

  • AM
  • PM
  • Daily

3.1 AM Highway Stats

3.1.1 AM Stats by volume groups

  1. Regional - Before ODME : The AM volumes before ODME process were extracted from the regional assignment. The total number of links with counts in the subarea are only 208. It is extremely hard to tell the year for this count although the field CNT_AM_10 might imply 2010 counts. The subarea wide %RMSE is 62% and the R2 is at 0.82. Most of the low volume and high volume groups shows R2 under 0.5

  2. Subarea - Before ODME : Theoretically this should be same as above. Since the ODME subarea OD matrix extraction process uses a re-assignment of the regional trips tables, a new loaded highway networks are generated. The assignment settings here match to regional assignment. The model volume by volume group (volBin) shows significant difference from the above table. The count in this table are updated with ODME counts for the subarea. The subarea wide R2 is slightly better at 0.84, however it is hard to tell if this is due to change in counts or more number of counts (256 counts instead of regional 208 counts as above).

  3. Subarea - After ODME : Shows ODME results, where R2 for subarea is at 0.96 and %RMSE is at 17%. The ODME feedback loop at 300th iteration shows %RMSE of 17.7% and ideally this should be under 14% meaning more number of iterations or more counts are required.

3.1.2 AM Stats by Facility Type

3.1.3 AM Stats Area Type

3.2 AM Scattered Plots

The plots below show a comparison between ODME counts and ODME results by volume group, facility type and area types.

3.2.1 AM Scattered Plots (by volume groups)

Most of the data points by volume groups align well, however, there are few outlier in 1 - 5,000 group and 10,000 - 20,000 group.

3.2.2 AM Scattered Plots (all volume groups)

Most of the data points fall close to the “best fit line”, however there are few that fall way off the line.

3.2.3 AM Scattered Plots (Facility Type group)

Most of the ODME results looks fine by Facility Type, however, types 1 and 7 show some link volumes way over or under. These links need to be studied further to determine if there is a count issue.

3.2.4 AM Scattered Plots (area Type groups)

Most of the ODME results looks fine by Area Type, however, types 3 and 4 show some link volumes that are way over or under. These links need to be studied further to determine if there is a count issue.

4. PM Period

Some of the stats: observed counts, model volumes, RMSE, %RMSE, R-Squared and Scattered Plots were computed by:

  • By Volume Group
  • By Facility Type
  • By Area Type

for the following time periods

  • AM
  • PM
  • Daily

4.1 PM Highway Stats

4.1.1 PM Stats by volume groups

4.1.2 PM Stats by Facility Type

4.1.3 PM Stats Area Type

4.2 PM Scattered Plots

4.2.1 PM Scattered Plots (by volume groups)

4.2.2 PM Scattered Plots (all volume groups)

4.2.3 PM Scattered Plots (Facility Type group)

4.2.4 PM Scattered Plots (area Type groups)

5. Daily

Some of the stats: observed counts, model volumes, RMSE, %RMSE, R-Squared and Scattered Plots were computed by:

  • By Volume Group
  • By Facility Type
  • By Area Type

for the following time periods

  • AM
  • PM
  • Daily

5.1 Daily Highway Stats

The daily highway stats were computed for before and after ODME links.

5.1.1 Daily Stats by volume groups

5.1.2 Daily Stats by Facility Type

5.1.3 Daily Stats Area Type

5.2 Daily Scattered Plots

5.2.1 Daily Scattered Plots (by volume groups)

5.2.2 Daily Scattered Plots (all volume groups)

5.2.3 Daily Scattered Plots (Facility Type group)

5.2.4 Daily Scattered Plots (Area Type groups)